{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "00_Keras_RNN_predictions_playground.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/tensorflow-without-a-phd/blob/master/tensorflow-rnn-tutorial/00_Keras_RNN_predictions_playground.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "_wxmLPc2YysH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# An RNN for short-term predictions\n",
        "This model will try to predict the next value in a short sequence based on historical data. This can be used for example to forecast demand based on a couple of weeks of sales data."
      ]
    },
    {
      "metadata": {
        "id": "3uk3AMTcYysJ",
        "colab_type": "code",
        "outputId": "cad7d5d5-665d-4ff5-a590-35114a1500a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import tensorflow as tf\n",
        "tf.enable_eager_execution()\n",
        "print(\"Tensorflow version: \" + tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensorflow version: 1.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Sk3oZyKsZLRf",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#@title Display utilities [RUN ME]\n",
        "\n",
        "from enum import IntEnum\n",
        "import numpy as np\n",
        "\n",
        "class Waveforms(IntEnum):\n",
        "    SINE1 = 0\n",
        "    SINE2 = 1\n",
        "    SINE3 = 2\n",
        "    SINE4 = 3\n",
        "\n",
        "def create_time_series(waveform, datalen):\n",
        "    # Generates a sequence of length datalen\n",
        "    # There are three available waveforms in the Waveforms enum\n",
        "    # good waveforms\n",
        "    frequencies = [(0.2, 0.15), (0.35, 0.3), (0.6, 0.55), (0.4, 0.25)]\n",
        "    freq1, freq2 = frequencies[waveform]\n",
        "    noise = [np.random.random()*0.2 for i in range(datalen)]\n",
        "    x1 = np.sin(np.arange(0,datalen) * freq1)  + noise\n",
        "    x2 = np.sin(np.arange(0,datalen) * freq2)  + noise\n",
        "    x = x1 + x2\n",
        "    return x.astype(np.float32)\n",
        "\n",
        "from matplotlib import transforms as plttrans\n",
        "\n",
        "plt.rcParams['figure.figsize']=(16.8,6.0)\n",
        "plt.rcParams['axes.grid']=True\n",
        "plt.rcParams['axes.linewidth']=0\n",
        "plt.rcParams['grid.color']='#DDDDDD'\n",
        "plt.rcParams['axes.facecolor']='white'\n",
        "plt.rcParams['xtick.major.size']=0\n",
        "plt.rcParams['ytick.major.size']=0\n",
        "\n",
        "def picture_this_1(data, datalen):\n",
        "    plt.subplot(211)\n",
        "    plt.plot(data[datalen-512:datalen+512])\n",
        "    plt.axvspan(0, 512, color='black', alpha=0.06)\n",
        "    plt.axvspan(512, 1024, color='grey', alpha=0.04)\n",
        "    plt.subplot(212)\n",
        "    plt.plot(data[3*datalen-512:3*datalen+512])\n",
        "    plt.axvspan(0, 512, color='grey', alpha=0.04)\n",
        "    plt.axvspan(512, 1024, color='black', alpha=0.06)\n",
        "    plt.show()\n",
        "    \n",
        "def picture_this_2(data, batchsize, seqlen):\n",
        "    samples = np.reshape(data, [-1, batchsize, seqlen])\n",
        "    rndsample = samples[np.random.choice(samples.shape[0], 8, replace=False)]\n",
        "    print(\"Tensor shape of a batch of training sequences: \" + str(rndsample[0].shape))\n",
        "    print(\"Random excerpt:\")\n",
        "    subplot = 241\n",
        "    for i in range(8):\n",
        "        plt.subplot(subplot)\n",
        "        plt.plot(rndsample[i, 0]) # first sequence in random batch\n",
        "        subplot += 1\n",
        "    plt.show()\n",
        "    \n",
        "def picture_this_3(predictions, evaldata, evallabels, seqlen):\n",
        "    subplot = 241\n",
        "    colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
        "    for i in range(8):\n",
        "        plt.subplot(subplot)\n",
        "        #k = int(np.random.rand() * evaldata.shape[0])\n",
        "        l0, = plt.plot(evaldata[i, 1:], label=\"data\")\n",
        "        plt.plot([seqlen-2, seqlen-1], evallabels[i, -2:])\n",
        "        l1, = plt.plot([seqlen-1], [predictions[i]], \"o\", color=\"red\", label='Predicted')\n",
        "        l2, = plt.plot([seqlen-1], [evallabels[i][-1]], \"o\", color=colors[1], label='Ground Truth')\n",
        "        if i==0:\n",
        "            plt.legend(handles=[l0, l1, l2])\n",
        "        subplot += 1\n",
        "    plt.show()\n",
        "    \n",
        "def picture_this_hist(rmse1, rmse2, rmse3, rmse):\n",
        "  colors = ['#4285f4', '#34a853', '#fbbc05', '#ea4334']\n",
        "  plt.figure(figsize=(5,4))\n",
        "  plt.xticks(rotation='40')\n",
        "  plt.title('RMSE: your model vs. simplistic approaches')\n",
        "  plt.bar(['RND', 'LAST', 'LAST2', 'Yours'], [rmse1, rmse2, rmse3, rmse], color=colors)\n",
        "  plt.show()\n",
        "\n",
        "def picture_this_hist_all(rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8):\n",
        "  colors = ['#4285f4', '#34a853', '#fbbc05', '#ea4334', '#4285f4', '#34a853', '#fbbc05', '#ea4334']\n",
        "  plt.figure(figsize=(7,4))\n",
        "  plt.xticks(rotation='40')\n",
        "  plt.ylim(0, 0.35)\n",
        "  plt.title('RMSE: all models')\n",
        "  plt.bar(['RND', 'LAST', 'LAST2', 'LINEAR', 'DNN', 'CNN', 'RNN', 'RNN_N'],\n",
        "          [rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8], color=colors)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RJ-1YaKQYysO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Generate fake dataset"
      ]
    },
    {
      "metadata": {
        "id": "pAhYNwMUYysP",
        "colab_type": "code",
        "outputId": "cb4e9e5d-8c37-42fc-d591-80b02ce17f93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        }
      },
      "cell_type": "code",
      "source": [
        "DATA_SEQ_LEN = 1024*128\n",
        "data = np.concatenate([create_time_series(waveform, DATA_SEQ_LEN) for waveform in Waveforms]) # 4 different wave forms\n",
        "picture_this_1(data, DATA_SEQ_LEN)\n",
        "DATA_LEN = DATA_SEQ_LEN * 4 # since we concatenated 4 sequences"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8Vl89iGQPsc0HIVsJi+urEAnjja/STYzYPGV7WM/L3AbMMyYBZEwIIxZtX5L4yAszAIAz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\nd37zTmGvFZybmMvUXZUcMK8Ri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6ytrRn5fN647rrrjOXl5VYOndgBfPrTnzb+4A/+wPjm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35nI97yxleFnMkd1V166/SXfuyywNedJMr0mLVerG/a3BOgVA8ZKWYQVRCJVZocE8aNYrCc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\nAgICAgICAgICAgICAhT+f87JjaPjOZcGAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Yt3TIQ6EYysS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Hyperparameters"
      ]
    },
    {
      "metadata": {
        "id": "FNw-tcLPYysS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "RNN_CELLSIZE = 32   # size of the RNN cells\n",
        "SEQLEN = 16         # unrolled sequence length\n",
        "BATCHSIZE = 32      # mini-batch size\n",
        "LAST_N = SEQLEN//2  # loss computed on last N element of sequence in advanced RNN model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bzLvRzgjYysU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Visualize training sequences\n",
        "This is what the neural network will see during training."
      ]
    },
    {
      "metadata": {
        "id": "IxTQ2-BRYysV",
        "colab_type": "code",
        "outputId": "d2c27d40-2e3e-4961-b8c4-33a0f0d7a55c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "cell_type": "code",
      "source": [
        "picture_this_2(data, BATCHSIZE, SEQLEN) # execute multiple times to see different sample sequences"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor shape of a batch of training sequences: (32, 16)\n",
            "Random excerpt:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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r2oXoSXd9DzqFhoYOeA9pJUnn7v4rKKsnI7eaCSP9GRPpS3ZRHXml9ZJIW6ju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LbCw235q86OBlH4ZT6O2NZtgc/jYMuqSHx2XYKKRjnWrgilOySCi8HncbAqJQTXq3pw/C8l2Ls5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\nIiIiIq2gQlpERERERESkFf4fo+w75BVIyN4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "SGh2-0E6YysZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Benchmark model definitions\n",
        "We will compare the RNNs against these models. For the time being you can regard them as black boxes."
      ]
    },
    {
      "metadata": {
        "id": "vVNz_D8bd8lA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# this is how to create a Keras model from neural network layers\n",
        "def compile_keras_sequential_model(list_of_layers, msg):\n",
        "  \n",
        "    # a tf.keras.Sequential model is a sequence of layers\n",
        "    model = tf.keras.Sequential(list_of_layers)\n",
        "    \n",
        "    # keras does not have a pre-defined metric for Root Mean Square Error. Let's define one.\n",
        "    def rmse(y_true, y_pred): # Root Mean Squared Error\n",
        "      return tf.sqrt(tf.reduce_mean(tf.square(y_pred - y_true)))\n",
        "    \n",
        "    print('\\nModel ', msg)\n",
        "    \n",
        "    # to finalize the model, specify the loss, the optimizer and metrics\n",
        "    model.compile(\n",
        "       loss = 'mean_squared_error',\n",
        "       optimizer = 'rmsprop',\n",
        "       metrics = [rmse])\n",
        "    \n",
        "    # this prints a description of the model\n",
        "    model.summary()\n",
        "    \n",
        "    return model\n",
        "  \n",
        "#\n",
        "# three very simplistic \"models\" that require no training. Can you beat them ?\n",
        "#\n",
        "\n",
        "# SIMPLISTIC BENCHMARK MODEL 1\n",
        "predict_same_as_last_value = lambda x: x[:,-1] # shape of x is [BATCHSIZE,SEQLEN]\n",
        "# SIMPLISTIC BENCHMARK MODEL 2\n",
        "predict_trend_from_last_two_values = lambda x: x[:,-1] + (x[:,-1] - x[:,-2])\n",
        "# SIMPLISTIC BENCHMARK MODEL 3\n",
        "predict_random_value = lambda x: tf.random.uniform(tf.shape(x)[0:1], -2.0, 2.0)\n",
        "\n",
        "def model_layers_from_lambda(lambda_fn, input_shape, output_shape):\n",
        "  return [tf.keras.layers.Lambda(lambda_fn, input_shape=input_shape),\n",
        "          tf.keras.layers.Reshape(output_shape)]\n",
        "\n",
        "model_layers_RAND  = model_layers_from_lambda(predict_random_value,               input_shape=[SEQLEN,], output_shape=[1,])\n",
        "model_layers_LAST  = model_layers_from_lambda(predict_same_as_last_value,         input_shape=[SEQLEN,], output_shape=[1,])\n",
        "model_layers_LAST2 = model_layers_from_lambda(predict_trend_from_last_two_values, input_shape=[SEQLEN,], output_shape=[1,])\n",
        "\n",
        "#\n",
        "# three neural network models for comparison, in increasing order of complexity\n",
        "#\n",
        "\n",
        "l = tf.keras.layers  # syntax shortcut\n",
        "\n",
        "# BENCHMARK MODEL 4: linear model (RMSE: 0.215 after 10 epochs)\n",
        "model_layers_LINEAR = [l.Dense(1, input_shape=[SEQLEN,])] # output shape [BATCHSIZE, 1]\n",
        "\n",
        "# BENCHMARK MODEL 5: 2-layer dense model (RMSE: 0.197 after 10 epochs)\n",
        "model_layers_DNN = [l.Dense(SEQLEN//2, activation='relu', input_shape=[SEQLEN,]), # input  shape [BATCHSIZE, SEQLEN]\n",
        "                    l.Dense(1)] # output shape [BATCHSIZE, 1]\n",
        "\n",
        "# BENCHMARK MODEL 6: convolutional (RMSE: 0.186 after 10 epochs)\n",
        "model_layers_CNN = [\n",
        "    l.Reshape([SEQLEN, 1], input_shape=[SEQLEN,]), # [BATCHSIZE, SEQLEN, 1] is necessary for conv model\n",
        "    l.Conv1D(filters=8, kernel_size=4, activation='relu', padding=\"same\"), # [BATCHSIZE, SEQLEN, 8]\n",
        "    l.Conv1D(filters=16, kernel_size=3, activation='relu', padding=\"same\"), # [BATCHSIZE, SEQLEN, 8]\n",
        "    l.Conv1D(filters=8, kernel_size=1, activation='relu', padding=\"same\"), # [BATCHSIZE, SEQLEN, 8]\n",
        "    l.MaxPooling1D(pool_size=2, strides=2),  # [BATCHSIZE, SEQLEN//2, 8]\n",
        "    l.Conv1D(filters=8, kernel_size=3, activation='relu', padding=\"same\"),  # [BATCHSIZE, SEQLEN//2, 8]\n",
        "    l.MaxPooling1D(pool_size=2, strides=2),  # [BATCHSIZE, SEQLEN//4, 8]\n",
        "    # mis-using a conv layer as linear regression :-)\n",
        "    l.Conv1D(filters=1, kernel_size=SEQLEN//4, activation=None, padding=\"valid\"), # output shape [BATCHSIZE, 1, 1]\n",
        "    l.Reshape([1,]) ] # output shape [BATCHSIZE, 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "04lT11FL7FBL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# instantiate the benchmark models\n",
        "model_RAND   = compile_keras_sequential_model(model_layers_RAND, \"RAND\") # Simplistic model without parameters. It needs no training.\n",
        "model_LAST   = compile_keras_sequential_model(model_layers_LAST, \"LAST\") # Simplistic model without parameters. It needs no training.\n",
        "model_LAST2  = compile_keras_sequential_model(model_layers_LAST2, \"LAST2\") # Simplistic model without parameters. It needs no training.\n",
        "model_LINEAR = compile_keras_sequential_model(model_layers_LINEAR, \"LINEAR\")\n",
        "model_DNN    = compile_keras_sequential_model(model_layers_DNN, \"DNN\")\n",
        "model_CNN    = compile_keras_sequential_model(model_layers_CNN, \"CNN\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "idWk7K3FYysn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Prepare datasets"
      ]
    },
    {
      "metadata": {
        "id": "ydOmIaCtYyso",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# training to predict the same sequence shifted by one (next value)\n",
        "labeldata = np.roll(data, -1)\n",
        "\n",
        "# cut data into sequences\n",
        "traindata = np.reshape(data, [-1, SEQLEN])\n",
        "labeldata = np.reshape(labeldata, [-1, SEQLEN])\n",
        "\n",
        "# make an evaluation dataset by cutting the sequences differently\n",
        "evaldata = np.roll(data, -SEQLEN//2)\n",
        "evallabels = np.roll(evaldata, -1)\n",
        "evaldata = np.reshape(evaldata, [-1, SEQLEN])\n",
        "evallabels = np.reshape(evallabels, [-1, SEQLEN])\n",
        "\n",
        "def get_training_dataset(last_n=1):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(\n",
        "      (\n",
        "          traindata, # features\n",
        "          labeldata[:,-last_n:SEQLEN] # targets: the last element or last n elements in the shifted sequence\n",
        "      )\n",
        "  )\n",
        "  # Dataset API used here to put the dataset into shape\n",
        "  dataset = dataset.repeat()\n",
        "  dataset = dataset.shuffle(DATA_LEN//SEQLEN) # shuffling is important ! (Number of sequences in shuffle buffer: all of them)\n",
        "  dataset = dataset.batch(BATCHSIZE, drop_remainder = True)\n",
        "  return dataset\n",
        "\n",
        "def get_evaluation_dataset(last_n=1):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(\n",
        "      (\n",
        "          evaldata, # features       \n",
        "          evallabels[:,-last_n:SEQLEN] # targets: the last element or last n elements in the shifted sequence\n",
        "      )\n",
        "  )\n",
        "  # Dataset API used here to put the dataset into shape\n",
        "  dataset = dataset.batch(evaldata.shape[0], drop_remainder = True) # just one batch with everything\n",
        "  return dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Q443hxmJ5LNe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Peek at the data"
      ]
    },
    {
      "metadata": {
        "id": "FN027ePq5Kxy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_ds = get_training_dataset()\n",
        "for features, labels in train_ds.take(10):\n",
        "  print(\"input_shape:\", features.numpy().shape, \", shape of labels:\", labels.numpy().shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CuNpxRbP_QYQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## RNN model [WORK REQUIRED]"
      ]
    },
    {
      "metadata": {
        "id": "XEyJklu-Yysi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "1. Train the model as it is, with a single dense layer (i.e. a linear model). Execute the Evaluation and Prediction cells and compare to simplistic models.\n",
        " * good enough ?\n",
        "1. Try training the benchmark models model_DNN and model_CNN (set `model=...` in training loop).\n",
        " * are multi-layer dense or convolutional neural networks better ?<br/>\n",
        "Do not forget to set model=model_RNN back in the trianing loop when you are done.\n",
        "1. Implement an RNN model with a single layer GRU cell: [`tf.keras.layers.GRU(RNN_CELLSIZE)`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU)<br/>\n",
        " followed by a linear regression layer (readout): [`tf.keras.layers.Dense(1)`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense)<br/>\n",
        " Note down the shapes at each layer and adjust as necessary with [`tf.keras.layers.Reshape([…], input_shape=[…])`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Reshape)<br/>\n",
        " Hints:\n",
        " * The first layer must have an `input_shape=[…]` parameter\n",
        " * tf.keras.layers.GRU implements an unrolled sequence of GRU cells. Its expected input shape is therefore [BATCHSIZE, SEQLEN, 1]\n",
        " * By default, tf.keras.layers.GRU only returns the last element in the output sequence. Its default output shape is [BATCHSIZE, RNN_CELLSIZE]\n",
        " * A dense layer with a single node has an output shape of [BATCHSIZE, 1]\n",
        " * Compare this with the shape of input and output data in the  \"Peek at the data\" cell above.\n",
        " * In Keras, tensor shapes have an implicit first dimension of BATCHSIZE. When you write input_shape=[SEQLEN,], the real shape is [BATCHSIZE, SEQLEN]\n",
        " * Pen, paper and fresh brain cells <font size=\"+2\">🤯</font> strongly recommened. Have fun ! \n",
        "\n",
        "1. Now add a second layer of GRU cells. The first layer must feed it the entire output sequence.\n",
        " * The syntax for that is  [`tf.keras.layers.GRU(RNN_CELLSIZE, return_sequences=True)`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU)\n",
        " * You can try three layers too but it will not perform significantly better.\n",
        "1. Another option for RNNs is to compute the loss on the last N elements of the predicted sequence instead of the last 1.\n",
        " * Copy your RNN model and rename it model_RNN_N\n",
        " * Modify the last GRU layer to output the full sequence with  [`tf.keras.layers.GRU(RNN_CELLSIZE, return_sequences=True)`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU)\n",
        " * The readout regression must now be replicated on all outputs in the sequence. The syntax for that is [`tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(1))`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/TimeDistributed)\n",
        " * Keep only the last N values in the sequence. This can be done with a lambda layer: [`tf.keras.layers.Lambda(lambda x: x[:,-LAST_N:SEQLEN,0])`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Lambda)\n",
        " * This model requires different target values during training and evaluation. Use get_training_dataset(last_n=LAST_N) and get_evaluation_dataset(last_n=LAST_N) to get them. Check shapes in the \"Peek at the data\" cell above.\n",
        " * A good value to try is LAST_N=SEQLEN//2. You can now train, evaluate, predict. Was is worth the extra effort <font size=\"+2\">🤯</font>?\n",
        "1. (Optional) In \"Benchmark\" cells at the end od the notebook, uncomment the lines corresponding to the RNN models you have implemented and run the bechmark.\n",
        " \n",
        "![deep RNN schematic](https://googlecloudplatform.github.io/tensorflow-without-a-phd/images/RNN1.svg)\n",
        "<div style=\"text-align: right; font-family: monospace\">\n",
        "  X shape [BATCHSIZE, SEQLEN, 1]<br/>\n",
        "  Y shape [BATCHSIZE, SEQLEN, 1]<br/>\n",
        "  H shape [BATCHSIZE, RNN_CELLSIZE*N_LAYERS]\n",
        "</div>"
      ]
    },
    {
      "metadata": {
        "id": "zPsQiS8pYysi",
        "colab_type": "code",
        "outputId": "623b9aaa-ea70-45f3-96ea-371bc8b77163",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "cell_type": "code",
      "source": [
        "model_layers_RNN = [\n",
        "    \n",
        "    tf.keras.layers.Dense(1, input_shape=[SEQLEN,]) # input shape needed on first layer only\n",
        "    #\n",
        "    # TODO: Replace the dummy Dense layer with your own layers\n",
        "    #\n",
        "]\n",
        "\n",
        "model_RNN = compile_keras_sequential_model(model_layers_RNN, \"RNN\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model  RNN\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_16 (Dense)             (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 17\n",
            "Trainable params: 17\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xKoWDXEC_InP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Training loop"
      ]
    },
    {
      "metadata": {
        "id": "KtqXxnUHhTHi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# You can re-execute this cell to continue training\n",
        "\n",
        "NB_EPOCHS = 3      # number of times the data is repeated during training\n",
        "steps_per_epoch = DATA_LEN // SEQLEN // BATCHSIZE\n",
        "\n",
        "model = model_RNN # model to train: model_LINEAR, model_DNN, model_CNN, model_RNN, model_RNN_N\n",
        "train_ds = get_training_dataset() # use last_n=LAST_N for model_RNN_N\n",
        "\n",
        "history = model.fit(train_ds, steps_per_epoch=steps_per_epoch, epochs=NB_EPOCHS)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "br7nVK9ijnCU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "N9RkMeEXW_qQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Evaluation"
      ]
    },
    {
      "metadata": {
        "id": "GyOzPv8Znu61",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Here \"evaluating\" using the training dataset\n",
        "eval_ds = get_evaluation_dataset()  # use last_n=LAST_N for model_RNN_N\n",
        "loss, rmse = model.evaluate(eval_ds, steps=1)\n",
        "\n",
        "# compare agains simplistic benchmark models that require no training\n",
        "ds = get_evaluation_dataset()\n",
        "_, rmse1 = model_RAND.evaluate(get_evaluation_dataset(), steps=1, verbose=0)\n",
        "_, rmse2 = model_LAST.evaluate(get_evaluation_dataset(), steps=1, verbose=0)\n",
        "_, rmse3 = model_LAST2.evaluate(get_evaluation_dataset(), steps=1, verbose=0)\n",
        "\n",
        "picture_this_hist(rmse1, rmse2, rmse3, rmse)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qJeFuhNRXCmq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Predictions"
      ]
    },
    {
      "metadata": {
        "id": "YpewCYd2Yys3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# execute multiple times to see different sample sequences\n",
        "subset = np.random.choice(DATA_LEN//SEQLEN, 8) # pick 8 eval sequences at random\n",
        "\n",
        "predictions = model.predict(evaldata[subset], steps=1) # prediction directly from numpy array\n",
        "picture_this_3(predictions[:,-1], evaldata[subset], evallabels[subset], SEQLEN)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0ScB9vM_Yys6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<a name=\"benchmark\"></a>\n",
        "## Benchmark\n",
        "Benchmark all the algorithms. This takes a while (approx. 10 min)."
      ]
    },
    {
      "metadata": {
        "id": "_EUH_ZsZKh1D",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# instantiate all models models again\n",
        "model_RAND   = compile_keras_sequential_model(model_layers_RAND, \"RAND\")\n",
        "model_LAST   = compile_keras_sequential_model(model_layers_LAST, \"LAST\")\n",
        "model_LAST2  = compile_keras_sequential_model(model_layers_LAST2, \"LAST2\")\n",
        "model_LINEAR = compile_keras_sequential_model(model_layers_LINEAR, \"LINEAR\")\n",
        "model_DNN    = compile_keras_sequential_model(model_layers_DNN, \"DNN\")\n",
        "model_CNN    = compile_keras_sequential_model(model_layers_CNN, \"CNN\")\n",
        "#model_RNN    = compile_keras_sequential_model(model_layers_RNN, \"RNN\")  # uncomment once you have implemented\n",
        "#model_RNN_N  = compile_keras_sequential_model(model_layers_RNN_N, 'RNN_N')  # uncomment once you have implemented"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pzUW6uMtYys6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "NB_EPOCHS = 10\n",
        "\n",
        "# train benchmark models\n",
        "for i, model in enumerate([model_LINEAR, model_DNN, model_CNN]):\n",
        "  print(\"Trainig \", [\"model_LINEAR\", \"model_DNN\", \"model_CNN\"][i])\n",
        "  model.fit(get_training_dataset(), steps_per_epoch=steps_per_epoch, epochs=NB_EPOCHS)\n",
        "\n",
        "# evaluate benchmark models\n",
        "rmses = []\n",
        "for model in [model_RAND, model_LAST, model_LAST2, model_LINEAR, model_DNN, model_CNN]:\n",
        "  _, rmse = model.evaluate(get_evaluation_dataset(), steps=1)\n",
        "  rmses.append(rmse)\n",
        "\n",
        "# Uncomment once you have implemented model_RNN and model_RNN_N\n",
        "#\n",
        "#print(\"Training \", \"model_RNN\")\n",
        "#model_RNN.fit(get_training_dataset(), steps_per_epoch=steps_per_epoch, epochs=NB_EPOCHS)\n",
        "#_, rmse = model_RNN.evaluate(get_evaluation_dataset(), steps=1)\n",
        "#rmses.append(rmse)\n",
        "#\n",
        "#print(\"Training \", \"model_RNN_N\")\n",
        "#model_RNN_N.fit(get_training_dataset(last_n=SEQLEN//2), steps_per_epoch=steps_per_epoch, epochs=NB_EPOCHS)\n",
        "#_, rmse = model_RNN_N.evaluate(get_evaluation_dataset(last_n=SEQLEN//2), steps=1)\n",
        "#rmses.append(rmse)\n",
        "\n",
        "print(rmses)\n",
        "picture_this_hist_all(*rmses)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2VgeUgL-Yys8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Copyright 2019 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "[http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0)\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    }
  ]
}